housing <- read.csv("/home/eeb177-student/Desktop/EEB-177/Lab-Work/exercise-8/Rgraphics/dataSets/landdata-states.csv")
head(housing[1:5])
## State region Date Home.Value Structure.Cost
## 1 AK West 2010.25 224952 160599
## 2 AK West 2010.50 225511 160252
## 3 AK West 2009.75 225820 163791
## 4 AK West 2010.00 224994 161787
## 5 AK West 2008.00 234590 155400
## 6 AK West 2008.25 233714 157458
hist(housing$Home.Value)
library(ggplot2)
ggplot(housing, aes(x = Home.Value)) + geom_histogram()
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
plot(Home.Value ~ Date,
data=subset(housing, State == "MA"))
points(Home.Value ~ Date, col="red",
data=subset(housing, State == "TX"))
legend(1975, 400000,
c("MA", "TX"), title="State",
col=c("black", "red"),
pch=c(1, 1))
ggplot(subset(housing, State %in% c("MA", "TX")),
aes(x=Date,
y=Home.Value,
color=State))+
geom_point()
hp2001Q1 <- subset(housing, Date == 2001.25)
ggplot(hp2001Q1,
aes(y = Structure.Cost, x = Land.Value)) +
geom_point()
ggplot(hp2001Q1,
aes(y = Structure.Cost, x = log(Land.Value))) +
geom_point()
hp2001Q1$pred.SC <- predict(lm(Structure.Cost ~ log(Land.Value), data = hp2001Q1))
p1 <- ggplot(hp2001Q1, aes(x = log(Land.Value), y = Structure.Cost))
p1 + geom_point(aes(color = Home.Value)) +
geom_line(aes(y = pred.SC))
p1 +
geom_point(aes(color = Home.Value)) +
geom_smooth()
## `geom_smooth()` using method = 'loess'
p1 +
geom_text(aes(label=State), size = 3)
# install.packages("ggrepel")
library("ggrepel")
p1 +
geom_point() +
geom_text_repel(aes(label=State), size = 3)
p1 +
geom_point(aes(size = 2),
color="red")
p1 +
geom_point(aes(color=Home.Value, shape = region))
## Warning: Removed 1 rows containing missing values (geom_point).
# Exercise 1
library(ggplot2)
dat <- read.csv("/home/eeb177-student/Desktop/EEB-177/Lab-Work/exercise-8/Rgraphics/dataSets/EconomistData.csv")
head(dat)
## X Country HDI.Rank HDI CPI Region
## 1 1 Afghanistan 172 0.398 1.5 Asia Pacific
## 2 2 Albania 70 0.739 3.1 East EU Cemt Asia
## 3 3 Algeria 96 0.698 2.9 MENA
## 4 4 Angola 148 0.486 2.0 SSA
## 5 5 Argentina 45 0.797 3.0 Americas
## 6 6 Armenia 86 0.716 2.6 East EU Cemt Asia
plot1 <- ggplot(dat, aes(x = CPI, y = HDI))
plot1 + geom_point(aes(color = Region, size = HDI.Rank))
args(geom_histogram)
## function (mapping = NULL, data = NULL, stat = "bin", position = "stack",
## ..., binwidth = NULL, bins = NULL, na.rm = FALSE, show.legend = NA,
## inherit.aes = TRUE)
## NULL
args(stat_bin)
## function (mapping = NULL, data = NULL, geom = "bar", position = "stack",
## ..., binwidth = NULL, bins = NULL, center = NULL, boundary = NULL,
## breaks = NULL, closed = c("right", "left"), pad = FALSE,
## na.rm = FALSE, show.legend = NA, inherit.aes = TRUE)
## NULL
p2 <- ggplot(housing, aes(x = Home.Value))
p2 + geom_histogram()
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
p2 + geom_histogram(stat = "bin", binwidth=4000)
housing.sum <- aggregate(housing["Home.Value"], housing["State"], FUN=mean)
rbind(head(housing.sum), tail(housing.sum))
## State Home.Value
## 1 AK 147385.14
## 2 AL 92545.22
## 3 AR 82076.84
## 4 AZ 140755.59
## 5 CA 282808.08
## 6 CO 158175.99
## 46 VA 155391.44
## 47 VT 132394.60
## 48 WA 178522.58
## 49 WI 108359.45
## 50 WV 77161.71
## 51 WY 122897.25
ggplot(housing.sum, aes(x=State, y=Home.Value)) +
geom_bar(stat="identity")
# Exercise 2
library(ggplot2)
dat <- read.csv("/home/eeb177-student/Desktop/EEB-177/Lab-Work/exercise-8/Rgraphics/dataSets/EconomistData.csv")
head(dat)
## X Country HDI.Rank HDI CPI Region
## 1 1 Afghanistan 172 0.398 1.5 Asia Pacific
## 2 2 Albania 70 0.739 3.1 East EU Cemt Asia
## 3 3 Algeria 96 0.698 2.9 MENA
## 4 4 Angola 148 0.486 2.0 SSA
## 5 5 Argentina 45 0.797 3.0 Americas
## 6 6 Armenia 86 0.716 2.6 East EU Cemt Asia
ggplot(dat, aes(x = CPI, y = HDI)) +
geom_point() +
geom_smooth(method = "lm")
# Exercise 2 Continued
ggplot(dat, aes(x = CPI, y = HDI)) +
geom_point() +
geom_line(stat = "smooth", method = "loess")
# Exercise 2 Bonus
ggplot(dat, aes(x = CPI, y = HDI)) +
geom_point() +
geom_smooth(span = .4)
## `geom_smooth()` using method = 'loess'
p3 <- ggplot(housing,
aes(x = State,
y = Home.Price.Index)) +
theme(legend.position="top",
axis.text=element_text(size = 6))
(p4 <- p3 + geom_point(aes(color = Date),
alpha = 0.5,
size = 1.5,
position = position_jitter(width = 0.25, height = 0)))
p4 + scale_x_discrete(name="State Abbreviation") +
scale_color_continuous(name="",
breaks = c(1976, 1994, 2013),
labels = c("'76", "'94", "'13"))
p4 +
scale_x_discrete(name="State Abbreviation") +
scale_color_continuous(name="",
breaks = c(1976, 1994, 2013),
labels = c("'76", "'94", "'13"),
low = "blue", high = "red")
p4 +
scale_color_continuous(name="",
breaks = c(1976, 1994, 2013),
labels = c("'76", "'94", "'13"),
low = "blue", high = "red")
p4 +
scale_color_gradient2(name="",
breaks = c(1976, 1994, 2013),
labels = c("'76", "'94", "'13"),
low = "blue",
high = "red",
mid = "gray60",
midpoint = 1994)
# Exercise 3
ggplot(dat, aes(x = CPI, y = HDI, color = "Region")) +
geom_point() +
scale_x_continuous(name = "Corruption Perception Index") +
scale_y_continuous(name = "Human Development Index") +
scale_color_manual(name = "Region of the world",
values = c("red"))
p5 <- ggplot(housing, aes(x = Date, y = Home.Value))
p5 + geom_line(aes(color = State))
(p5 <- p5 + geom_line() +
facet_wrap(~State, ncol = 10))
p5 + theme_linedraw()
p5 + theme_light()
p5 + theme_minimal() +
theme(text = element_text(color = "turquoise"))
theme_new <- theme_bw() +
theme(plot.background = element_rect(size = 1, color = "blue", fill = "black"),
text=element_text(size = 12, family = "Serif", color = "ivory"),
axis.text.y = element_text(colour = "purple"),
axis.text.x = element_text(colour = "red"),
panel.background = element_rect(fill = "pink"),
strip.background = element_rect(fill = "orange"))
p5 + theme_new
housing.byyear <- aggregate(cbind(Home.Value, Land.Value) ~ Date, data = housing, mean)
ggplot(housing.byyear,
aes(x=Date)) +
geom_line(aes(y=Home.Value), color="red") +
geom_line(aes(y=Land.Value), color="blue")
library(tidyr)
home.land.byyear <- gather(housing.byyear,
value = "value",
key = "type",
Home.Value, Land.Value)
ggplot(home.land.byyear,
aes(x=Date,
y=value,
color=type)) +
geom_line()
dat <- read.csv("/home/eeb177-student/Desktop/EEB-177/Lab-Work/exercise-8/Rgraphics/dataSets/EconomistData.csv")
# Build inital plot
pc1 <- ggplot(dat, aes(x = CPI, y = HDI, color = Region))
pc1 + geom_point()
# Add in the trendline
(pc2 <- pc1 +
geom_smooth(aes(group = 1),
method = "lm",
formula = y ~ log(x),
se = FALSE,
color = "red")) +
geom_point()
# Make adjustments
pc2 +
geom_point(shape = 1, size = 4)
(pc3 <- pc2 + geom_point(shape = 1, size = 2.5, stroke = 1.25))
# Label the points on the graph
pointsToLabel <- c("Russia", "Venezuela", "Iraq", "Myanmar", "Sudan",
"Afghanistan", "Congo", "Greece", "Argentina", "Brazil",
"India", "Italy", "China", "South Africa", "Spane",
"Botswana", "Cape Verde", "Bhutan", "Rwanda", "France",
"United States", "Germany", "Britain", "Barbados", "Norway", "Japan",
"New Zealand", "Singapore")
(pc4 <- pc3 +
geom_text(aes(label = Country),
color = "gray20",
data = subset(dat, Country %in% pointsToLabel)))
library("ggrepel")
(pc4 <- pc3 +
geom_text_repel(aes(label = Country),
color = "gray20",
data = subset(dat, Country %in% pointsToLabel),
force = 10))
# Label the countries, etc.
dat$Region <- factor(dat$Region,
levels = c("EU W. Europe",
"Americas",
"Asia Pacific",
"East EU Cemt Asia",
"MENA",
"SSA"),
labels = c("OECD",
"Americas",
"Asia &\nOceania",
"Central &\nEastern Europe",
"Middle East &\nnorth Africa",
"Sub-Saharan\nAfrica"))
pc4$data <- dat
pc4
library(grid)
(pc5 <- pc4 +
scale_x_continuous(name = "Corruption Perceptions Index, 2011 (10=least corrupt)",
limits = c(.9, 10.5),
breaks = 1:10) +
scale_y_continuous(name = "Human Development Index, 2011 (1=Best)",
limits = c(0.2, 1.0),
breaks = seq(0.2, 1.0, by = 0.1)) +
scale_color_manual(name = "",
values = c("deepskyblue4",
"deepskyblue3",
"deepskyblue2",
"darkcyan",
"brown3",
"brown4")) +
ggtitle("Corruption and Human development"))
library(grid)
(pc6 <- pc5 +
theme_minimal() +
theme(text = element_text(color = "gray20"),
legend.position = c("top"),
legend.direction = "horizontal",
legend.justification = 0.1,
legend.text = element_text(size = 11, color = "gray10"),
axis.text = element_text(face = "italic"),
axis.title.x = element_text(vjust = -1),
axis.title.y = element_text(vjust = 2),
axis.ticks.y = element_blank(),
axis.line = element_line(color = "gray40", size = 0.5),
axis.line.y = element_blank(),
panel.grid.major = element_line(color = "gray50", size = 0.5),
panel.grid.major.x = element_blank()
))
